1. Technical Field
The present invention relates to a method and system for early detection of incipient faults in electric motors in general, and in particular to a neural network-based method and system for detecting incipient electrical, mechanical and electromechanical faults in electric motors.
2. Description of the Related Art
Approximately 60% of all incipient motor failures can be attributed to mechanical and electromechanical causes. Thus, many efforts have been made towards the early detection of incipient mechanical and electromechanical motor faults, and the most widely accepted approach for detecting incipient mechanical and electromechanical faults are vibration monitoring and motor current monitoring, respectively. The monitoring of the negative sequence of motor current and motor impedance is also widely used for the detection of incipient electrical motor faults. However, none of the above-mentioned approaches provides adequate indicators for the detection of incipient electrical, mechanical and electromechanical faults. For example, frequent changes in the temporal behavior of a power supply cause imbalances, which can obscure a fault signature and lead one to believe that there is an incipient electrical fault occurring even though the root-cause of the problem is supply imbalances. As such, the effectiveness of the conventional methods for detecting incipient electrical, mechanical and electromechanical faults in electric motors is significantly diminished.
Consequently, it would be desirable to provide an improved method and system for the early detection of incipient electrical, mechanical and electromechanical faults in electric motors.
In accordance with a preferred embodiment of the present invention, current and voltage values for one or more phases of an electric motor are measured during motor operations. A set of current predictions is then determined based on the measured voltage values and an estimate of motor speed values of the electric motor via a neural network-based current predictor. Next, a set of residuals is generated by combining the set of current predictions with the measured current values. A set of fault indicators is subsequently computed from the set of residuals and the measured current values. Finally, a determination is made as to whether or not there is an incipient electrical, mechanical, and/or electromechanical fault occurring based on the comparison result of the set of fault indicators and a set of predetermined baseline values.
All objects, features, and advantages of the present invention will become apparent in the following detailed written description.